Demand Response Mechanism Research Articles

Demand Response Strategies among Power Distribution Companies for Efficient Power System Management in Nigeria

This study investigates the demand response strategies employed by power distribution companies (DisCos) in Nigeria and their implications for efficient power system management. As the Nigerian power sector grapples with persistent inefficiencies and reliability issues, implementing effective demand response mechanisms emerges as a crucial strategy for improving overall performance. The research employs a quantitative methodology, utilizing a structured questionnaire administered to a sample of 316 stakeholders across various DisCos in Nigeria. Findings reveal a significant relationship between the implementation of demand response strategies and the efficiency of the power system (p < 0.05), indicating that such strategies can enhance operational efficiency. Moreover, the adoption of advanced metering infrastructure is shown to significantly improve the effectiveness of these strategies, underscoring the importance of technological integration in power distribution. However, the study also identifies key challenges faced by DisCos, such as regulatory hurdles and inadequate infrastructure, which hinder the adoption of demand response initiatives.

A stackelberg game-based programming approach for industrial steam systems incorporating renewable energy considering demand response

Fossil fuels account for 37 % of industrial combustion, primarily used for steam production. Given the significant role of steam in industrial processes, industrial steam systems (ISS) offer a critical opportunity to accelerate the transition from fossil fuels to renewable energy by integrating renewable sources. Currently, ISS optimization focuses mainly on internal improvements, often neglecting stakeholder interactions and the inherent unpredictability of renewable energy sources. This paper introduces a novel bi-level programming approach based on Stackelberg game theory to address the balance between ISS and user interests. A new ISS model is proposed that integrates multiple renewable energy sources while accounting for their intermittency. Revenue models for energy servers (ES) and consumers (EC) are developed, and demand response mechanisms and real-time pricing are incorporated into the bi-level programming model. In the designed bi-level ISS (B-L-ISS) model, the upper level optimizes energy conversion equipment and pricing, while the lower level aims to minimize users' energy costs. The Karush-Kuhn-Tucker (KKT) conditions are used to reformulate the bi-level problem into a single-level programming problem with equilibrium constraints. Validation on a real coastal industrial steam system shows that the proposed method integrates renewable energy four times more effectively than traditional methods, reducing CO2 emissions by 15,790 tons annually, while balancing the interests of both the steam system and its users.

An optimal scheduling strategy for electricity-thermal synergy and complementarity among multi-microgrid based on cooperative games

Multi-microgrid (MMG) systems provide an effective way to convert renewable energy into other forms of energy for low-carbon utilization. However, the coupling of multi-energy and renewable energy brings a challenge to the stable operation and dispatch of the system. Therefore, an electricity-thermal-gas-hydrogen MMG complementary optimization model based on peer-to-peer (P2P) electricity-thermal synergy is proposed. The model focuses on the coupling and conversion of electricity, hydrogen, and thermal through a hydrogen energy storage system (HESS) combined with renewable energy, and incorporates integrated demand response (IDR) and ladder-type carbon trading mechanism (LCTM). Subsequently, based on the cooperative game and the alternating direction multiplier method (ADMM), a multi-energy synergistic and complementary optimal scheduling strategy is proposed, which increases the flexibility of multi-energy sharing. In addition, a revenue allocation optimization strategy that takes into account the fairness and renewable energy consumption rate is proposed to achieve the stability of the alliance system operation. Finally, the simulation results show the effectiveness of the proposed model and strategy, realizing the complementary sharing of regional multi-energy, enabling the expansion of the boundaries of optimal energy allocation, further improving the low-carbon economic operation capability of MMG, and reducing the dependence of the system on the energy market.

Solar Power and Demand Response for Greening Indian Lignite Power Plants: A CO2 Reduction Initiative

This research paper delves into the prospect of curbing carbon dioxide (CO2) emissions by strategically deploying solar photovoltaic (PV) systems and orchestrating demand response (DR) mechanisms within Indian lignite power plants (LPP). The study responds to the critical imperative of mitigating greenhouse gas (GHG) emissions originating from coal-based electricity generation, a matter of substantial consequence in the context of climate change. In pursuit of optimal solar PV system allocation, this research employs the particle swarm optimization (PSO) technique, considering a spectrum of factors including solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation. The primary objective is to minimize CO2 emissions while maximizing the integration of solar PV and curtailing power losses, all while accounting for the intermittent nature of solar power and the dynamic nature of demand. The proposed approach is rigorously tested on the IEEE 33 bus system, supplied by the LPP. The results convincingly demonstrate a remarkable reduction in CO2 emissions, amounting to 29.69%, following the implementation of the proposed approach. This research presents a concrete step towards a more sustainable and environmentally friendly energy landscape, offering valuable insights for policymakers and stakeholders in the energy sector.

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties

Improving the efficiency of home energy management (HEM) is of great significance in reducing resource waste and prompting renewable energy consumption. With the development of advanced HEM systems and internet-of-things technology, more residents are willing to participate in the electricity market through a demand response mechanism, which can not only reduce the electricity bill but also stabilize the operation of the main grid through peak shaving and valley filling. However, the uncertainties from household appliance parameters, user behaviors, penetrated renewable energy, and electricity prices render the HEM problem a non-trivial task. Although previous deep reinforcement learning (DRL) methods have no requirement for system dynamics due to their model-free and data-driven merits, the unrestricted action space may violate the physical constraints of various devices, and few works have concentrated on this area before. To solve the above challenges, this paper proposes a novel interior-point policy optimization-based multi-agent DRL algorithm to optimize the home energy scheduling procedure while guaranteeing safety during its operation. Besides, a time-series prediction model based on a non-stationary Transformer neural network is proposed to predict the future trend of solar generation and electricity price, which can provide the agents with abundant information to make better decisions. The superior performance of our proposed predictive-control coupled method is demonstrated through extensive numerical experiments, which achieves more precise prediction results, near-zero constraint violations, and better trade-offs between cost and safety than several benchmark methods.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

Photovoltaic sizing assessment for microgrid communities under load shifting constraints and endogenous electricity prices: A Stackelberg approach

Renewable energy resources are crucial for decarbonizing the energy sector. Distributed energy resources, such as renewable generation by microgrids, aid the transition to net-zero systems. As microgrids proliferate, renewable generation often exceeds self-supply capacities, necessitating either the sale of excess electricity or the implementation of demand-response strategies. The optimal approach for microgrids (selling or shifting demand) hinges on electricity prices. This article examines the interplay between microgrids, demand response, photovoltaic investments, and pricing mechanisms. A novel Stackelberg model is proposed to minimize microgrid investment and operational costs. The upper-level problem determines energy community investments and demand response levels based on electricity prices, while the lower-level problem addresses market dispatch and endogenous pricing for microgrids. Case studies with varying battery storage levels, demand response limits, and pricing mechanisms are conducted. The research results illustrate the responsiveness of market transactions to supply conditions and pricing. Key findings show that microgrid flexibility in demand response reduces grid dependency, prompting significant investments in solar energy and battery storage, driven by economic incentives, especially under spot market conditions. Investments in solar energy more than double when microgrids trade energy at spot prices compared to flat prices.

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives

AbstractCarbon emissions limit the output of traditional fuel‐fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short‐term electric power and energy balance optimization scheduling method with low‐carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short‐term electric power and energy balance of the system under a dual perspective, along with the electric‐carbon coupling mechanism of the dynamic scheduling on the source‐load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load‐side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low‐carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.

New Customized Bidirectional Real-Time Pricing Mechanism for Demand Response in Predictive Home Energy Management System

New Customized Bidirectional Real-Time Pricing Mechanism for Demand Response in Predictive Home Energy Management System

Dynamic Consensus-Based ADMM Strategy for Economic Dispatch with Demand Response in Power Grids

This paper introduces a dynamic consensus-based economic dispatch (ED) algorithm utilizing the Alternating Direction Method of Multipliers (ADMM) to optimize real-time pricing and generation/demand decisions within a decentralized energy management framework. The increasing complexity of modern energy markets, driven by the proliferation of Distributed Energy Resources (DER) and variable demands from hybrid electric vehicles, necessitates a departure from traditional centralized dispatch methods. This research proposes a novel ADMM-based solution tailored for non-responsive and responsive demand units that integrates demand response mechanisms to adaptively manage real-time fluctuations while enhancing security and privacy through distributed data management. The testing of the algorithm on the IEEE 39 bus system under various load conditions over 24 h demonstrated the algorithm’s effectiveness in handling traditional and renewable energy sources, particularly highlighting the economic benefits of shifting controllable loads to periods of low-cost renewable availability. The findings underscore the algorithm’s potential to reduce energy costs, enhance energy efficiency, and offer a scalable solution across diverse grid systems, contributing significantly to advancing global energy policy and sustainable management practices.

Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

Analysis of Carbon Emission Reduction with Using Low-Carbon Demand Response: Case Study of North China Power Grid

The power sector is the single industry with the largest carbon emission in China, the carbon emission of which accounts for more than 40% of China’s total carbon emissions. In relevant research on the simulation of power system operation, current studies focus more on energy conservation and economical operation, while few consider the low-carbon optimization of the power system from the perspective of carbon emissions. In addition, in relevant research on carbon reduction in the power system, current studies focus more on controlling the direct carbon emission of the source side and less on the indirect carbon emissions of the load side, which focus on the reverse effect of a user’s electricity consumption behavior on the carbon reduction goals of the power system. This article delved into a deterministic simulation model of power system operation based on time series load curves and proposed a carbon reduction mechanism called the low-carbon demand response mechanism, which guides users to actively respond and reduce the carbon emission of power systems. In addition, this article conducted an empirical analysis based on the planning data of the North China Power Grid. To minimize carbon emissions, a simulation of low-carbon optimization operation for the North China Power Grid in 2040 was carried out. Then, based on the simulation results, an analysis of the carbon reduction benefits of low-carbon demand response was carried out. Ultimately, the empirical analysis verified that low-carbon optimization operation and low-carbon demand response technology possess significant carbon reduction potential for the power system.

Optimal demand response for a virtual power plant with a hierarchical operation framework

There are significant uncertainties and variations in demand response under different operating conditions and incentives. These challenges hinder virtual power plants (VPPs) from fully leveraging the regulatory capabilities of flexible resources. To address this issue, this study introduces an optimal demand response (DR) approach for VPPs, featuring a hierarchical operational framework that accounts for the uncertainty in user participation in DR. First, a refined DR model is developed, incorporating response characteristic parameters based on the DR mechanism. Second, a two-stage distributionally robust optimization model is formulated within a market trading framework for energy scheduling of VPPs. This model aims to minimize operational costs while considering uncertainties in DR. Additionally, the ambiguity set for the uncertainty probability distributions is constructed using a data-driven method. Finally, the study presents an optimal DR disaggregation method that aims to maximize aggregator satisfaction and accounts for the impact of load rebound on the optimization results. Simulation results demonstrate that the proposed method significantly reduces the deviation in demand response by 40 % and enhances the profit of the VPP by 20.7 %.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

Coordinated Operation Strategy for Equitable Aggregation in Virtual Power Plant Clusters with Electric Heat Demand Response Considered

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source-load-storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario.

Low carbon optimization dispatching of energy intensive industrial park based on adaptive stepped demand response incentive mechanism

In order to reduce the operation cost and carbon emissions of the energy intensive industrial park (EIIP) system, a low-carbon optimal dispatching method considering the parameter adaptive demand response (DR) incentive mechanism is proposed. Firstly, the carbon capture system (CCS) and power to gas (P2G) equipment are introduced into the EIIP. Meanwhile, according to the different regulation types of loads’ DR capacity, a stepped DR incentive mechanism model including three parameters of interval length, compensation base price and price growth rate is proposed; Secondly, a bi-layer optimization model with the objective of minimizing the operation cost at the energy supply side and maximizing the energy efficiency at the load side of the EIIP is established. The DR Mechanism parameter optimization layer is constructed outside the two-layer optimization model, so as to establish a three-layer optimization model of the stepped DR mechanism considering parameter adaptation; The Kepler optimization algorithm (KOA) is used to optimize and implement the adaptive changes of mechanism parameters, further demonstrate the mechanism in the low-carbon optimal dispatching of EIIP; Finally, the simulation results based on multiple scenarios show that the proposed method improves the effectiveness of EIIP system operation economy and reduces carbon emissions.

Demand response mechanisms: A new debate on internalizing power generation sector negative technical spillovers

The presence of technical and operational constraints within the power generation sector gives rise to negative externalities, resulting in spillover effects that can significantly impact the economic efficiency of power markets. This article endeavors to address these challenges by focusing on the demand side of power systems. Within the article, a demand response-based approach is presented as a means to internalize the negative technical externalities spillovers originating from power plants in day-ahead power markets. This approach involves the responsive demand generating a new service that opens up opportunities for energy interchange within the power market. To effectively internalize these spillovers, the introduced service incorporates both quantity and price adjustments. A discriminatory pricing mechanism for this service is proposed, accompanied by a bi-level optimization problem aimed at determining the elastic responsive demand fraction necessary to deliver the service. Utilizing the proposed scheme facilitates attaining the first-best optimal market solution by alleviating negative spillovers inherent in sub-optimal second-best solutions arising from operational constraints in power plants. Numerical results presented in the article serve to underscore the efficacy of the proposed method, demonstrating its ability to function seamlessly and contribute to the optimization of power market outcomes.

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

Integration of RES and AI for Grid Distribution Network Energy Management

In an era where sustainability and efficient resource utilization are paramount, optimizing energy management in grid distribution networks is a top priority. This research introduces a cutting-edge approach that harnesses the power of Renewable Energy Sources (RES), Artificial Intelligence (AI), Long Short-Term Memory (LSTM) networks, Quadratic Regression, and Demand Response mechanisms for Grid Distribution Network Energy Management. By fuzing these state-of-the-art technologies, we unlock the potential to revolutionize energy load forecasting with unprecedented precision and foresight. LSTM models, enriched with historical load data, weather conditions, and demand-response patterns, empower us to anticipate grid requirements with exceptional accuracy. Our approach consistently achieves an average Mean Absolute Percentage Error (MAPE) below 5% and a Root Mean Square Error (RMSE) under 2% for load predictions with an overall grid distribution efficiency is 98%, surpassing conventional forecasting methodologies. Furthermore, the integration of demand response strategies results in a remarkable 20% peak shaving ratio, contributing to a 15% reduction in energy demand during high-demand periods. This research not only enhances smart grid technologies but also ushers in a more resilient, adaptive, and eco-friendly energy infrastructure for the future.